Niche underwriters have always comforted themselves with the thought that disruption from Big Data, AI and machine learning (buzzwords misused almost as much as blockchain) is only for giant classes of business. The theory is they will be able to hide in their niches, protected from the mayhem and ensuing jobs carnage by the lack of Big Data. They cling to the hope that these classes just aren't big enough to justify the expense of all that data science.

By Graham Elliott, CEO of Azur

However, with the advent of increasingly sophisticated SaaS applications, the growing commoditisation of data and new, API-rich, configurable platforms, there is an increasing feeling that this hope may be misguided. But all is not lost; we have a long way to go before robots are so sophisticated that they displace the underwriter at the box in Lloyd's.

People are fond of quoting Bill Gates, who said “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.” What is often missed off this aperçu is the final sentence: “Don't let yourself be lulled into inaction.” This begs the question of what action should we take now to prepare us for the 10-year change?

And the answer is learn how to use Augmented Underwriting.

Avoid the misconceptions

Think of the parallels with virtual and augmented reality. In the early days, the wow factor was the virtual world - sophisticated software with headsets that could convince you that you’re hurtling down a black run on a snowboard, when in fact you were standing on a piece of wood at an exhibition centre, flailing around to the huge amusement of your colleagues. These are stunning ideas and point to a future world where the virtual and real worlds will become increasingly hard to tell apart.

The most energy in the sector, and the most commercialisation of the technology, has taken place in the world of augmented reality.

The systems don’t replace the physical and the human, they merely enhance human ability. A computer to help you play golf better doesn’t hit the ball for you, it just gives you tips on how to hit it better yourself.

This is already happening in the asset management world, where many businesses have a quantitative underlay, with computers enhancing and improving humans’ ability to absorb and analyse tons of data to help make better investment decisions.

This has just not happened yet in insurance, and certainly not in the sub-scale niche classes that employ so many people handling underwriting in an almost totally manual way. But it is about to, and there is a stark choice for these companies. If they decide to play, the investment is long and hard and the payback uncertain; if they decide they just don’t believe it, the end result will almost certainly be a 20th century business that quickly loses its ability to compete against more agile real time systems.

The future
So what is Augmented Underwriting? There are three steps to heaven if you want to deploy it. First off, you have to have a fit-for-purpose core operating system with all your data in one place before you can really implement it. It’s amazing how many highly trained and expensive data scientists and actuaries still spend 80% of their time acquiring, combining and cleansing the data before they can get around to harvesting any insights from it. If you have a legacy stack with a poor data model and data all over the place, this explains why you haven’t yet got any real value out of all those clever people.

One of the key benefits of augmented underwriting is the ability to respond quickly to changes in behaviour, competition, demand and appetite. How can you operate in a real time environment if your stack is dependent on batch processing or delegated authority business coming in on a bordereau 8 weeks after the risks have bound?

Once you have this stable platform in place, the second key component of augmented underwriting is data enrichment. This is, first and foremost, to ensure that the user experience (UX) for the brokers and/or the end user is as slick as possible. Without this, brokers will quickly find that they are not seeing the risks on which they’d like to quote. It should not be necessary to ask some poor client upwards of 55 questions in order to give them a quote for their insurance cover.

The problem the technology has to solve is that there’s a need for asymmetrical UX: the end insured wants minimal hassle, but the capital wants maximum information to price the risk accurately and make an underwriting profit. Data enrichment on a modern platform solves this issue. And finally, niche classes, by definition aren't capable of delivering Big Data-type solutions horizontally.

Data enrichment gives them more data points which they need to implement true augmented underwriting. It provides Big Data vertically, giving a treasure trove of data about each risk and making it feasible to deploy the machine learning techniques.

And, lastly, brokers and insurers can now begin to use data science to help assess, grade and price risks. However, this doesn’t mean the machine makes all the decisions on underwriting. There is the need for human contact, especially in the intermediated broker channel, to handle things like negotiation and offer deeper understanding of clients and their needs. But without the help of machines to learn from patterns in the data, cognitive bias can creep in. The computer is there to guide and help, suggesting when a risk is less likely to convert and when the underwriter might want to increase the discount. Alternatively, it can suggest where there’s a strong likelihood of a claim, allowing the underwriter to increase rates to cater for the increased risk.

This powerful combination, enabled by modern technology, will give early adopters a substantial market edge. Portfolios will be underwritten more efficiently, with fewer questions to bother the client or the broker. There’ll be more data to help understand and mitigate the risk, and configurable systems where rates can be adjusted on a live basis to allow underwriters to manage exposure.

All of this comes as standard with real time management information, and at a cost that makes it possible to cater for niche lines that have hitherto been too small to warrant the investment. Just imagine if you are the only airline at Gatwick that can set seat prices on a daily basis, while your rivals have to decide their pricing at the beginning of the year. Augmented underwriting will become a serious competitive advantage, and well before the robots have taken all the jobs.